Yield variability prediction by remote sensing sensors with different spatial resolution
Yield variability prediction by remote sensing sensors with different spatial resolution
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DOI：
10.1515/intag-2016-0046
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发表时间：
2017-01
影响因子：
2.2
通讯作者：
J. Kumhálová;Š. Matějková
中科院分区：
文献类型：
--
作者：
J. Kumhálová;Š. Matějková
摘要目前，遥感传感器在农作物监测和产量预测中非常流行。本文介绍了如何使用中等（Landsat卫星数据）和非常高（QuickBird和WorldView-2卫星数据）空间分辨率的卫星图像，以及GreenSeeker手持式作物传感器，来估计产量和作物生长变异性。选择冬大麦（2007年和2015年）和冬小麦（2009年和2011年）是因为Landsat卫星图像和QuickBird或WorldView-2图像在同一时间段的无云数据可用。非常高的空间分辨率图像被重新采样到更差的空间分辨率。归一化差异植被指数是从每个卫星图像数据集得出的，它也是用GreenSeeker手持式作物传感器测量的，仅在2015年。结果表明，每个卫星图像数据集可以用于产量和植物变异性估计。然而，与作物产量相比，在后期物候期获得的图像获得了更好的结果，例如2007年- BBCH 59 -平均相关系数为0.856，2011年- BBCH 59-0.784。由于测量方法不同，GreenSeeker手持式农作物传感器不适合产量估计。
Abstract Currently, remote sensing sensors are very popular for crop monitoring and yield prediction. This paper describes how satellite images with moderate (Landsat satellite data) and very high (QuickBird and WorldView-2 satellite data) spatial resolution, together with GreenSeeker hand held crop sensor, can be used to estimate yield and crop growth variability. Winter barley (2007 and 2015) and winter wheat (2009 and 2011) were chosen because of cloud-free data availability in the same time period for experimental field from Landsat satellite images and QuickBird or WorldView-2 images. Very high spatial resolution images were resampled to worse spatial resolution. Normalised difference vegetation index was derived from each satellite image data sets and it was also measured with GreenSeeker handheld crop sensor for the year 2015 only. Results showed that each satellite image data set can be used for yield and plant variability estimation. Nevertheless, better results, in comparison with crop yield, were obtained for images acquired in later phenological phases, e.g. in 2007 - BBCH 59 - average correlation coefficient 0.856, and in 2011 - BBCH 59-0.784. GreenSeeker handheld crop sensor was not suitable for yield estimation due to different measuring method.